Data Governance — Aligning Technical and Business Approaches

Copyright Global Data Strategy, Ltd. 2021
Data Governance – Aligning Business and
Technical Approaches
Donna Burbank
Global Data Strategy, Ltd.
October 28th, 2021
Follow on Twitter @donnaburbank
Twitter Event hashtag: #DAStrategies

Global Data Strategy, Ltd. 2021
Donna Burbank
2
• Recognized industry expert in information
management with over 25 years of
experience in data strategy, information
management, data modeling, metadata
management, and enterprise architecture
• Managing Director at Global Data Strategy,
Ltd., an international information
management consulting company that
specializes in the alignment of business
drivers with data-centric technology
• Worked with dozens of Fortune 500
companies worldwide in the Americas,
Europe, Asia, and Africa and speaks
regularly at industry conferences
• Excellence in Data Management Award
from DAMA International
• Past President and Advisor to the DAMA
Rocky Mountain chapter
• Co-author of several books on data
management
• Regular contributor to industry
publications
• She can be reached at
donna.burbank@globaldatastrategy.com
Donna is based in Boulder, Colorado, US
Follow on Twitter @donnaburbank
@GlobalDataStrat

DATAVERSITY Data Architecture Strategies
• January Emerging Trends in Data Architecture – What’s the Next Big Thing?
• February Building a Data Strategy - Practical Steps for Aligning with Business Goals
• March Data Modeling Case Study – Business Data Modeling at Kiewit
• April Master Data Management – Aligning Data, Process, and Governance
• May Data Architecture, Solution Architecture, Platform Architecture – What’s the Difference?
• June Enterprise Architecture vs. Data Architecture
• July Best Practices in Metadata Management
• August Data Quality Best Practices (with guest Nigel Turner)
• September Data Modeling Techniques
• October Data Governance: Aligning Technical & Business Approaches
• December Data Architecture for Digital Transformation
3
This Year’s Lineup

What We’ll Cover Today
4
• Data Governance can have a varied definition, depending on the audience. To many, data
governance consists of committee meetings and stewardship roles.
• …To others, it focuses on technical data management and controls.
• Holistic data governance combines both of these aspects, and a robust data architecture and
associated diagrams can be the “glue” that binds business and IT governance together.
• This webinar will provide practical tips and hands-on exercises for aligning data architecture &
data governance for business and IT success.

Global Data Strategy, Ltd. 2021 5
A Successful Data Strategy links Business Goals with Technology Solutions
“Top-Down” alignment with
business priorities
“Bottom-Up” management &
inventory of data sources
Managing the people, process,
policies & culture around data
Coordinating & integrating
disparate data sources
Leveraging & managing data for
strategic advantage
Data Governance is Part of a Wider Data Strategy
www.globaldatastrategy.com

Data Governance
Data Governance is critical in supporting the data-
driven business
• 76% have a current data governance initiative in
place or are planning one in the near future
• 86% consider data security a priority
• >50% identified improved collaboration
through using a defined data architecture
6
Data Governance improves collaboration and increases data accountability & protection
* based on research from a 2019 DATAVERSITY survey on “Trends in Data Management” by Donna Burbank and Michelle McKnight

What is Data Governance?
The DAMA Data Management Body of Knowledge (DMBOK), defines data governance as the following:
• The exercise of authority, control and shared decision-making (planning, monitoring and
enforcement) over the management of data assets.
7
DMBOK Definition
Exercise of authority, control Shared decision-making
Carrot
Yin
Yang

8
What my friends think I do
What I think I do
What my mom thinks I do
What my coworkers think I do
What society thinks do
DATA GOVERNANCE
What I actually do
Driving the
Success of
the Business

The Need for Data Governance
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Reducing Risk
(Defense)
Increasing Opportunity
(Offense)
Data Governance
Improving
Efficiency
Driving
Collaboration & Accountability
• Regulations require the need to protect & account
for customer data (e.g. GDPR, PCI)
• Actions & decisions driven by data require quality
& accountability of information.
• Data is a strategic asset and must be managed
accordingly for competitive advantage.
• Enabling a Data-driven organization through
quality, governed data.
• Well-governed data allows teams to spend less
time looking for, managing, and cleaning data,
and more time using it for strategic advantage.
• Governance helps reduce redundancy and
duplication of effort across systems and teams.
• Clear roles & responsibilities ensure that
individuals take active accountability for the
quality & protection of data.
• Teams work together to prioritize the best use of
data for the benefit of the organization and its
customers.

“Offense” vs. “Defense”
• Focused on Creating Opportunity
• Improving Profitability
• Increasing Revenue
• Improving Customer Satisfaction
• Competitive Advantage
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Which style of data governance fits your organization?
Offense Defense
• Focused on Reducing Risk
• Compliance & Regulation
• Avoiding Audits or Fines
• Fraud Detection
• Security & Privacy
On which end of the spectrum is your organization?

The Diversity of Data Governance
• The Scope of Data Governance is diverse:
• From the “touchy feely” people side
• To the “detailed nerdy” technical side
• i.e. There is something for everyone….And everyone is likely to hate some part… ☺
11
Something for everyone…to love…or hate…or somewhere in between
I love working with
people!
I love creating
databases!

Tools &
Platforms
Applying a Structured Data Governance Framework
Vision & Strategy
Organization &
People
Processes &
Workflows
Data Management
& Measures
Culture & Communication
Copyright Global Data Strategy, Ltd. 2021
A structured Data Governance Framework helps align business and IT for long-term success.

Building the Data Governance Framework
13
Vision & Strategy
Organization &
People
Processes &
Workflows
Data Management &
Measures
Tools & Technology
Culture &
Communications
Is there a clear understanding
of the strategic goals of your
organization & the need for
enterprise data governance?
Who are the key data
stakeholders within and
outside your organization?
Do business process design
and operations management
take data needs into account?
Has key data been identified,
defined and analyzed?
Is there a coherent data
architecture in place to define
and guide how data is
captured, processed, stored
and used?
Has the importance of data
been communicated across the
organization? Is there a data
communications plan?
How does your organization
rely on data – now and in the
future?
Who are the primary data
producers, consumers &
modifiers?
Are there any specific data
management / improvement
processes in place?
Have data models been built –
conceptual / logical / physical?
What primary IT systems and
platforms are used to store
and process key data?
Is the value of good data
management understood and
championed by senior
managers?
What impact are data
problems currently having on
your organization?
Are individuals formally
accountable for data
ownership?
Are there issue and workflow
management processes to
address data problems?
Has the relationship between
business processes and data
been mapped?
Do design gateways exist to
ensure data needs are taken
into account in new &
modified platforms?
Do all employees and third
parties receive data awareness
and improvement education
and training?
Do you have a data governance
policy?
Are employees trained in good
data management practices?
Has there been any analysis of
the efficiency and
effectiveness of how data is
managed within operational
business processes?
Are data shortcomings known,
measured & recorded?
What specialist data
management tools are
currently in use?
Are there communication
channels for communicating
best practice in data
management?
What are the overall expected
benefits of better data
governance?
Are there any channels
through which data
shortcomings can be
highlighted and investigated?
How does the business and IT
interact to manage data
improvement?
Are there are formal standards
& rules specifying how data
should be managed and
improved?
What metadata is captured
and stored?
Are there internal success
stories that could be used to
promote better data
management across the
organization?

a number of respondents mentioned Data Governance in their comments as a way to align the various stakeholders around
common goals
Who is Driving Data Management in an Organization?
• While Technical Roles still lead Data
Management activities, Business
Stakeholders are playing a larger part.
• From those who listed “Other”, Data
Governance Lead was a common
response.
14
A number of respondents mentioned Data Governance as a way to align various stakeholders around common goals
* based on research from a 2019 DATAVERSITY survey on “Trends in Data Management” by Donna Burbank and Michelle McKnight

Data Stewardship
• Data Stewards are often found, not made.
• Often there are “hidden heroes” in the organization who are champions for data within their
organization.
• Many are pleased to finally have a “voice” and some official responsibility
15
Finding the right person for the job

Data Governance Roles - Examples
16
Executive Sponsor Business Data Owner Business Data Steward Technical Data Steward
• Promotes Data Driven Culture
• Champions Best Practices
• Advocate with ELT and Board
• Escalation Point for Key Issues
• Represents the data needs for a
particular functional area
• Defines key KPIs & data elements
• Defines key business rules
• Sets Data Quality Metrics &
Thresholds
• Responsible for the day-to-day
management and quality of data
• Subject Matter Expert (SME) for
a given business domain
• Aligns with the Data Owner to
support business rules and to
align with key KPIs
• Digital/IT expert for a given
business unit
• Subject matter expert for a given
system and its usage
• Aligns with Business Data
Stewards to ensure technical
needs are met
While there is no “one size fits all” approach to governance roles, the following are some examples:

Mapping Organizational Capability
• Organizational Capability, Organizational Structure, and Roles are key to any Data Strategy
17
Aligning to Organizational Capabilities
e.g. From Plan to Production to Sales & Distribution
Designing Org Structures for Data-Centric Efforts
e.g. Aligning Data Governance to Individual Culture

Example: Federated, Global Manufacturing & Retail
18
Data
Innovation
Council
Supply
Chain
R&D
Etc
Etc
Operations
Consumer
Marketing
Data Architecture
Digital Transformation Team
“Digital Update”
Data Innovation Teams
Agile Project
Teams
Agile Project
Teams
Agile Development Cycles

Finding the Right Balance
19
Human
Automated
Resolve at Source Resolve via Post-Processing
• Business Process Change
• Policies & Procedures
• Governance Steering Committees
• End User Training
• Industry Advisory Councils
• Data definition & glossary
• Data Quality Working Groups
• Application-Driven Data Entry & Workflow
• Application-level data validation
• Database-level data validation & integrity
(data models)
• Data Quality tool validation at source
• Data Cleansing Tools and/or SQL
• ETL (Data Warehouse)
• Data Stewardship
• “Conscious Disregard”
Proactive Business Management Reactive Business Management
Proactive Technical Management Reactive Technical Management
• Data Audit & Dashboards
• External Data Sources

Data Governance Tools
There is no “one size fits all” data governance tool. Pick the solutions that match your uses cases & audiences.
20

The Critical Role of Data Architecture & Governance
21
A high-level data architecture provides the roadmap for data strategy & associated governance.
Business data model
What data do we prioritize? Where is this data used?
Business process models
Where is this data stored?
Data architecture diagram
What rules apply to this data?
Business rules & policy
What is the quality of the data?
Data quality dashboard
Business Glossary
This architecture provides a guide for small, targeted projects for business value to add additional detail.
What data best
supports our Brokers?
What data best supports
our Customers?
What data can we use to
best Price our Policies?
Home Auto
Commercial
What external data can we use
for business advantage?

Metadata Makes Data Governance Actionable
• Metadata can help take the business rules & definitions defined in policies and make them
actionable in physical systems, maintaining a lineage & audit trail.
22
Policies & Procedures Technical Implementation Audit & Lineage

Metadata Management Tools
• The following are common architectural options for metadata management within & between organizations.
• There is no “one size fits all” approach.
• They can be used together within the same organization.
23
Central, Enterprise-wide
Metadata Catalogue / Repository
Metamodel(s)
Metadata Storage
(Database)
Population
Interfaces
Matching &
Reuse Logic
Publication & Sharing
Reports Web Portal Integration & Export
Tool or Purpose-Specific
Repository
Business Glossary
ETL Tool
Data Modeling Tool
BI Tool
Etc
Data Dictionary
Database
Metadata Exchange &
Registry
Information Sharing & Standards

Data Models are a Key Part of Data Governance
24
Data Models are critical to data governance at the conceptual, logical, and physical levels.

Process Models
• Process models are a helpful tool for describing core business processes.
• “Swimlanes” outline organizational considerations
• Data can be mapped to key business processes to understand creation & usage of information.
25
Identifying key data dependencies in core business processes

Customer Journey Map
• A customer journey map
outlines key phases of the
customer in their “journey”.
• They are similar to a process
model, but with a different
focus & perspective.
• Creating a data overlay is a
helpful way to see the key
data touched at each point
in the journey.
• Journey maps can be
created for other industries
as well, e.g. Student,
Patient, etc.
26

CRUD Matrix -- DRUC?
Product
Development
Supply Chain
Accounting
Marketing Finance
Product Assembly Instructions C R
Product Components C R
Product Price C U R
Product Name C U,D
Etc.
27
Create, Read, Update, Delete
• CRUD Matrices shows where data is Created, Read, Updated or Deleted across the
various areas of the organization
• This can be a helpful tool in data governance & data quality.

Data Governance through Data Architecture
• A major Retail Vendor wanted to become a data-driven company
• Enhancing the customer experience by mapping the Customer Journey to the Data Lifecycle
• Using IoT product data to improve product design & customer service
• Optimizing product supply chain & delivery
• Developing a Tactical Data Strategy determined that
• Master Data Management was needed to manage customer contact data throughout the Customer Journey
• Data Governance was required to manage data across organizational siloes: product development, marketing, sales, etc.
• Data Architecture was needed to understand the data ecosystem: data flow diagrams, data models, process models, etc.
28
Using Data to Build Customer Loyalty & Increase Sales
Customer Support
Customer Discovery &
Purchase
Product Delivery &
Tracking
Product Usage Tracking Product Development

Summary
• Orchestrate the people, process,
technology, & culture required to support
your Data Governance program.
• Pick the right tools for the right job – there is
no “one size fits all” solution.
• Build “quick wins” into your roadmap to
provide business value through every stage
of your architecture development

DATAVERSITY Data Architecture Strategies
• January Emerging Trends in Data Architecture – What’s the Next Big Thing?
• February Building a Data Strategy - Practical Steps for Aligning with Business Goals
• March Data Modeling Case Study – Business Data Modeling at Kiewit
• April Master Data Management – Aligning Data, Process, and Governance
• May Data Architecture, Solution Architecture, Platform Architecture – What’s the Difference?
• June Enterprise Architecture vs. Data Architecture
• July Best Practices in Metadata Management
• August Data Quality Best Practices (with guest Nigel Turner)
• September Data Modeling Techniques
• October Data Governance: Aligning Technical & Business Approaches
• December Data Architecture for Digital Transformation
30
This Year’s Lineup

Who We Are: Business-Focused Data Strategy
Maximize the Organizational Value of Your Data Investment
In today’s business environment, showing rapid time to value for
any technical investment is critical.
But technology and data can be complex. At Global Data Strategy,
we help demystify technical complexity to help you:
• Demonstrate the ROI and business value of data to your
management
• Build a data strategy at your pace to match your unique culture
and organizational style.
• Create an actionable roadmap for “quick wins”, which building
towards a long-term scalable architecture.
Global Data Strategy’s shares experience from some of the largest
international organizations scaled to the pace of your unique team.
www.globaldatastrategy.com
Global Data Strategy has worked with organizations globally in the
following industries:
Finance · Retail · Social Services · Health Care · Education · Manufacturing
· Government · Public Utilities · Construction · Media & Entertainment ·
Insurance …. and more

Global Data Strategy, Ltd. 2021 www.globaldatastrategy.com
Questions?
Thoughts? Ideas?
32

Data Governance — Aligning Technical and Business Approaches

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